Optimizing Diverse Team Collaboration and Supply Chain Agility with AI in Emerging Markets
Keywords:
AI-driven risk management, Supply chain agility, Process re-engineering, Predictive analytics, Structural equation modelingAbstract
The study investigates the effects of AI-based risk management (AIRM) on supply chain agility (SCA) and re-engineering capabilities (RP) to enhance resilience in volatile market environments. Using structural equation modeling (SEM) across a diverse sample, we find that AIRM significantly boosts SCA (β = 0.45, p < 0.001) by enabling rapid, data-driven adjustments to demand fluctuations. Additionally, AIRM has a substantial impact on RP (β = 0.53, p < 0.001), allowing firms to integrate flexible processes and new technologies, thereby enhancing long-term adaptability. RP also mediates the relationship between AIRM and SCA, with an indirect effect of β = 0.22 (p < 0.01), highlighting that re-engineered processes amplify AIRM’s impact on agility. These results suggest that organizations, particularly in sectors with high demand variability, should invest in AIRM and process re-engineering to achieve both immediate responsiveness and sustained supply chain flexibility. This study provides a quantitative framework for integrating AIRM into supply chain operations, offering strategic insights for building adaptive, resilient supply chains.
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